Shopify Data Scientist Interview Guide (Updated for 2024)

Shopify is an e-commerce platform that allows small and growing businesses to create and manage their online stores. Shopify handles a wide variety of services, from payment processing to storefront creation to logistics to SEO.

Data Scientists at Shopify involve themselves in a variety of tasks, such as the following:

• Identifying and solving business problems using data analysis.
• Building and deploying machine learning models for automation, predicting customer behavior, and improving the performance of Shopify’s services.
• Creating data-driven visualizations for reporting and generating insights.

Getting ready for an Data Scientist interview at Shopify? The Shopify Data Scientist interview spans 10 to 12 question topics. In preparing for the interview:

• Know what skills are necessary for Shopify Data Scientist roles.
• Gain insights into the Data Scientist interview process at Shopify.
• Practice real Shopify Data Scientist interview questions.

Interview Query regularly analyzes interview experience data, and we’ve used that data to produce this guide, with sample interview questions and an overview of the Shopify Data Scientist interview.

Typically, interviews at Shopify vary by role and team, but their Data Scientist interviews follow a fairly standardized process across these question topics.

The interview process goes through approximately five rounds with the following structure:

1. Preliminary screening
2. Technical interview
3. Coding interview
4. Life story / behavioral interview
5. The technical deep dive (talking about past projects)

Throughout the process, keep in mind that Shopify values real work scenarios and doesn’t rely on trivia or brain teasers. Your ultimate goal should be to demonstrate your potential as a Data Scientist and your capacity to add value to the company.

Note: We’ve gathered this data from parsing thousands of interview experiences sourced from members.

The Life Story interview is typically conducted by a recruiter or hiring manager, and it is an open conversation that doesn’t follow any rigid structure. The interviewer will ask you questions about your life, your values, your goals, and your experiences. They may also ask you to share stories about times when you have faced challenges or overcome obstacles.

The goal of the Life Story interview is to get to know you as a person and to understand how your past experiences have shaped you. Shopify is looking for people who are passionate, driven, and who have a strong sense of purpose. They also want to find people who are a good fit for their company culture.

If you are invited to a Life Story interview, be prepared to share your personal story. This is your chance to show the interviewer who you are and why you would be a good fit for Shopify. Be honest, be authentic, and be yourself.

Remember, these interviews are not just for Shopify to learn about you, but also for you to learn about Shopify. Come prepared with thoughtful questions about the company, the team, and the role. This shows your genuine interest in the position and can help you decide if Shopify is the right place for you.

At Interview Query, we love to hear from those who’ve successfully landed jobs in the data science field. To help the rest of our community, we’re sharing their career path stories and approach to interview preparation.

Interview Query recently spoke with Michael, a self-taught data scientist who just landed a job at Shopify. Michael talked about his approach to data science interviews, what he learned during Shopify’s data science interview, and advice he would share with new grads.

What’s your background?

I recently joined Shopify as a Staff Data Scientist at Shopify. Previously, I worked for an e-commerce company called jane.com, which is like an Etsy-type marketplace, where I was a Lead Data Scientist. Before that, I was at a tech consulting company called Rangle.io in Toronto, where I’m from. And then before that, I was at KPMG on their data science consulting team.

How did you prepare for interviews?

I started preparing about four or five months before the interviews. I focused on a lot of A/B questions, as well as basic statistics. Machine learning was also a focus.

I primarily used Interview Query. I was most interested in A/B testing questions. So I ended up working on a lot of A/B testing questions and watching the video solutions. I wanted to focus on the acute gaps in my knowledge, so the data science assessments were helpful in identifying those gaps.

I also did some mock interviews with Interview Query coaches with three different mentors and had some great interviews because everyone brought different perspectives. The interviews were helpful because they all provided feedback. The feedback I got was basically you clearly know what you’re doing, but you could organize your thoughts better and be more concise.

What mistakes did you make?

One mistake I made was focusing too much on more complex, high-level questions, as opposed to just really getting a solid understanding of the fundamentals. As I started doing interviews before my Shopify interview, I wasn’t making sure I could answer those core questions really well, and that kind of hurt me. This was my first time doing big tech interviews, so it took me a bit of trial and error to get it right.

I also found I was overpreparing and sticking too rigidly to the frameworks. Basically, I would overanalyze the questions and give very rigid responses. What I learned is that you don’t have to get the exact right answer. The interview process is more about showing your thought process and your approach.

Interviewers want to see if you can go through a problem logically and communicate well. I had to learn this personally after going through a couple of failed interviews.

What’s Shopify’s interview process like?

The actual meeting process took about a month, and it was fairly standard, a lot of meeting people. I got an offer about two months after the initial interviews.

Shopify is really big on cultural fit, I think more so than other companies. So the Shopify interview process is friendlier and more practical than other companies. The questions emulate things you would do in the day-to-day job. They asked Python, SQL analytics pipeline building, and then some ML case studies, but it was very pragmatic. If you had done the work, you could do well.

What do you think led to the offer?

The biggest factor, I think, was that I was relaxed and I was having fun. I focused on showing my expertise in e-commerce. Instead of memorizing A/B testing frameworks or machine learning methodologies, I leveraged my experience, and I described my approach to the practical problems that were proposed.

That doesn’t work for every company, but if the interview is very practical, I think it’s helpful to focus on your expertise and enjoy yourself.

Do you have interview advice for new grads?

Build things. Build things. Build things. Work on building your portfolio and fill it with practical projects. In most interviews, they want to see if you’ve actually used data science techniques and that you enjoyed doing the work. Practical applications are paramount in most of these interviews. So that’s my main piece of advice.

I’d also say domain knowledge is a big thing. Show you understand. Like, if you were interviewing at Square on the fraud detection team, show your interest in anomaly detection and add those projects to your portfolio. In the interview, you can say I built these four models using these test data sets. I deployed it using this platform. Show that you thought through the steps that you would take on the day-to-day job.

What would you share with someone transitioning careers?

I actually transitioned into data science from a management consulting background. So I’m pretty much self-taught.

For people like me, with unconventional backgrounds, interview prep is pretty essential. People see my liberal arts bachelor’s, and they say, “Can he do what he says he can do?” So I spend a lot of time making sure that I’m very well prepared for every potential question I might get in an interview. That’s how I counteract my lack of formal training.

Hopefully, you’ve found Michael’s story at Shopify helpful! Here are a few Shopify data scientist interview questions to further reinforce your skills and knowledge:

1. Given a list of strings of letters from a to z, create a function, sum_alphabet, that returns a list of the alphabet sum of each word in the string.
2. How would you determine whether sending an email blast is beneficial?
3. What metrics should be analyzed to identify the reasons behind a decline in user comments?
4. How would you build a job recommendation feed?
5. What retention rate would be required to surpass the revenue from the non-subscription price?
6. If your manager runs an A/B test with 20 different variants and finds one significant result, would you be suspicious of the findings?
7. How would you determine if providing free shipping on a controlled group would increase conversions?
8. How would you design a relational database that stores information about a song?
9. How would you analyze the cause of revenue loss given in this dataset?
10. Let’s say you’re setting up the analytics tracking for a web app. How would you create a schema to represent client click data on the web?